Agentic AI Development
AI agents that finish the job, not just the demo.
We design and build agents that plan, call your tools, check their own work, and run in production with accuracy, cost, and risk under control.
Most AI agents never make it past the demo
A prototype that works on ten hand-picked examples is a weekend of work. An agent that handles ten thousand real requests, with real permissions, at a cost you can defend, is an engineering project. The gap between the two is where most AI initiatives stall.
Agentic AI development is the discipline of closing that gap. A large language model is given a goal, a set of tools, and the freedom to plan its own steps: it reads data, calls APIs, verifies results, and keeps going until the task is done. Making that reliable takes software engineering, not prompt tweaking, and that is the part we do.
Where agent projects break
- Nobody can say how accurate it is.
Without an evaluation set, every prompt change is a guess and every release is a risk you cannot size.
- It is allowed to do too much.
An agent holding broad API keys is one injected instruction away from an incident.
- It costs more than the work it replaces.
Unbounded loops, oversized context, and the largest model on every step add up quickly at real volume.
- It fails silently.
With no trace of what the agent read, decided, and called, a wrong answer cannot be explained or fixed.
AI Agents We Build
Each one designed around a single job, measured against it, and connected to the systems you already run
Customer support agents
Agents that resolve requests instead of deflecting them. They answer from your documentation, look up orders and accounts through your APIs, take the actions your policy allows such as refunds, changes, and bookings, and hand over to a person with the full context when a case needs judgement.
Knowledge and research assistants
Retrieval augmented generation (RAG) over your documents, tickets, code, and databases. Every answer cites its sources, respects each user's access rights, and says so when the information is not there.
Operations and back office agents
Intake, triage, reconciliation, data entry, and reporting, run end to end by agents that work inside the tools your team already uses, with approvals where money or customers are involved.
Multi-agent systems
For work too broad for one agent: an orchestrator that plans and delegates, specialist agents that research, write, and act in parallel, and reviewer agents that check the output before it ships. We add agents only when a single one has measurably hit its limit.
Not Every Problem Needs an Agent
Agents are the most flexible design and the most expensive to run and verify. We recommend the simplest one that does the job.
Single model call
Classification, extraction, summarising, drafting. Fast, cheap, and easy to test. Many problems described as needing an agent end here.
Orchestrated workflow
Code controls the order and the model handles the language at each step. Predictable, auditable, and the right choice when the process is already well defined.
Autonomous agent
Investigation, open-ended support, tasks that span several systems. The model decides the next step, inside limits you set. Most flexible, and the one that most needs evaluation and guardrails.
What Production Grade Means
Six things every agent we ship has, and most prototypes do not
Context engineering
An agent is only as good as what it can see. We design retrieval, memory, and context so the model gets the right facts at the right step, and nothing that wastes tokens or leaks data it should not have.
Tool and API design
Agents act through tools. We wrap your systems as narrow, well described tools with validated inputs, using function calling and the Model Context Protocol (MCP), so the agent can do its job and nothing beyond it.
Evaluation before release
We build a test set from your real cases, score every change against it, and calibrate automated graders against human reviewers. You see accuracy as a number that moves, not an impression from a demo.
Guardrails and permissions
Least-privilege credentials, human approval for irreversible actions, input and output checks, and defences against prompt injection. The agent's authority is decided by your policy, not by its prompt.
Observability
Every run is traced: what the agent read, what it decided, which tools it called, and what it cost. When something goes wrong you can replay it, find the cause, and add the case to the test set.
Cost and latency control
Model routing that sends easy steps to small fast models, prompt caching, step and token budgets, and parallel tool calls. The agent stays quick enough to use and cheap enough to justify.
How We Deliver Agentic AI Projects
One workflow at a time, with a measured result at every stage
Discover
We pick one workflow worth automating, map its data and systems, define what a correct result is, and decide whether it needs an agent at all.
Prototype
A working agent on your real data, connected to real tools in a sandbox, so you judge actual output early instead of slides.
Evaluate
We measure accuracy, cost, and speed against the test set, fix the failure cases, and repeat until the numbers meet the bar agreed in discovery.
Ship and improve
Staged rollout with monitoring and human escalation. Production failures feed back into the test set, so the agent gets better with use.
Models and tooling we work with
- Claude
- GPT
- Gemini
- Open-weight models
- Model Context Protocol (MCP)
- LangGraph
- RAG and vector search
- Python
- Node.js
- AWS
Agentic AI Questions, Answered
What is the difference between an AI agent and a chatbot?
A chatbot answers a message. An AI agent is given a goal and works towards it: it plans the steps, calls tools and APIs, checks the results, and continues until the task is complete or it needs a person. A chatbot tells a customer how to change a booking. An agent changes the booking.
Do we need an agent, or would a simpler workflow do?
Often a simpler design is enough, and we will say so. If the steps are known in advance, an orchestrated workflow is cheaper, faster, and easier to audit. Agents earn their cost when the path cannot be scripted ahead of time, for example investigations, open-ended support, or tasks that cross several systems.
How do you stop an agent from making mistakes or taking harmful actions?
In layers. The agent only receives the tools and credentials its task requires. Irreversible actions such as payments, deletions, and outbound messages need human approval. Inputs and outputs are checked, including for prompt injection. And every release is scored against an evaluation set built from your real cases before it reaches users.
Which models and frameworks do you use?
We are model-agnostic and choose per task: a frontier model where reasoning matters, a small fast model where it does not. We work with Claude, GPT, Gemini, and open-weight models, and connect them to your systems through function calling and the Model Context Protocol (MCP). We keep the architecture portable so you can switch models as the market moves.
What does a first engagement look like?
We start with one workflow, not a platform. Discovery defines the task and what a correct result is. Then we build a working agent on your real data and measure it against a test set. You decide whether to roll it out based on measured results, before committing to anything broader.
How do you keep running costs under control?
Cost is designed in from the start: routing simple steps to smaller models, caching repeated context, limiting steps and tokens per task, and tracking cost per completed task in monitoring. You see what each task costs and can compare it with the work it replaces.
Is our data safe, and who owns what you build?
Your data stays in your environment and with the model providers you approve, under their no-training terms. The agent respects your existing access controls, so users only reach what they are already allowed to see. The code, prompts, and evaluation sets we build are yours.
Ready to Transform Your Business?
Let's discuss your project and explore how we can help you achieve your goals